We've heard some commentary suggesting that printing is as much an art as it is a science. But when you're changing a digital design to include an encoded signal, and then sending it to print, you would like to remove as much guesswork as possible. It becomes important to accurately predict or model how various print colors will interact or “blend” with one another, e.g., when overprinted onto one another on various different types of substrates. An inaccurate prediction at the design stage can result in a design that is subpar in terms of poor encoded signal robustness (e.g., low encoded signal detection) and high signal visibility. That is, a poor ink blend prediction can result in an aesthetically ugly design that doesn't detect very well. Moreover, it's important to establish an objective grade for the encoded signal instead of relying on guesswork or instinct.
We've developed technology, methods and systems to help predict how an information signal encoded design will behave, e.g., in terms of robustness and/or visibility, once printed on various different substrates.
“Digital watermarking” is a form of signal encoding. For purposes of this disclosure, the terms “digital watermark(ing),” and “watermark” are used interchangeably. We sometimes use the terms “embedding,” “embed,” “signal encoding,” and “encoding” (and variants thereof) to mean modulating or transforming data representing imagery, audio or video to include information therein. For example, signal encoding may seek to encode an information signal (e.g., a plural bit payload or a modified version of such, e.g., a 2-D error corrected, spread spectrum signal) in a host signal. This can be accomplished, e.g., by modulating a host signal (e.g., image, video or audio) in some fashion to carry or convey the information signal. One way to modulate a host signal is to combine or overprint a first color with additional colors. One or more of the colors, or the color combination itself, may carry or represent the information signal. Another way to impart encoded data to an image, artwork or design is to print a so-called sparse digital watermark pattern, e.g., in open space or over a background. Additional details regarding sparse watermarking is found in assignee's U.S. Pat. No. 9,635,378, which is incorporated herein by reference in its entirety. We use the terms “decode,” “detect,” and “read” (and variants thereof) interchangeably to mean detecting and/or recovering an encoded signal such as a digital watermark.
Some of the present assignee's work in steganography, signal encoding and digital watermarking is reflected, e.g., in U.S. Pat. Nos. 9,565,335, 9,449,357; 9,401,001; 9,380,186; 8,199,969, 6,947,571; 6,912,295; 6,891,959. 6,763,123; 6,718,046; 6,614,914; 6,590,996; 6,449,377, 6,408,082; 6,345,104; 6,122,403 and 5,862,260; and US Published Patent Application Nos. US 2017-0024840 A1, US 2016-0316098 A1, US 2016-0275639 A1 and US 2013-0329006 A1. Each of the patent documents in this paragraph is hereby incorporated by reference herein in its entirety.
This disclosure describes an image processing system and related methods which provide improved encoded signal prediction in view of real-world constraints, e.g., printing press variations, ink overprint estimations, object geometry, etc.
This disclosure also details prediction systems, apparatus and methods to predict how various colors will interact or “blend” with one another, e.g., when printed over various different types of substrates and color layers, in connection with signal encoding utilizing so-called spot colors and process colors.
This disclosure further describes technology for establishing an objective multi-sided grade for packages including encoded signals.
Now for some color science.
Spot colors may include premixed inks for use instead of or in addition to process color inks. In many print environments, each spot color ink typically uses its own printing plate on a print press. Spot colors can be used instead of or in addition to process colors for better color accuracy, better color consistency, and colors outside of process ink gamut and for technologies which are prone to specific printing errors. A common spot color system is PANTONE (http://www.pantone.com/). The PANTONE system defines several hundred different inks.
Process colors can be printed using a combination of four standard process inks: Cyan, Magenta, Yellow and Black (CMYK), and sometimes more than four inks are used, e.g., adding orange (O), violet (V), and green (G). Considering that every color used in some printing presses uses its own plate, it is highly impractical to print using every color in a design. Process color printing was developed, in part, to address this impracticality, since most colors can be accurately approximated with a combination of these four process colors, CMYK (or seven if using OVG). To create a process color which includes multiple inks, overprinting can be used.
Similar to CMYK, it is usually possible to print a percentage of a given spot color. We refer to printing less than 100% of a spot color as “screening” (or “a screen”) the spot color or as a “spot color tint”. There are sometimes advantages to using process color equivalent tint. The process color equivalent tint can be a combination of CMYK percentages which produce an approximation color for an original spot color or spot color tint. Process colors can be printed with, e.g., half tone dots.
Overprinting is the process of printing one or more colors on top of another in the reproduction of a design. Because of physical differences between inks and substrate, the result of printing directly onto the substrate versus onto another ink may differ and can be considered in a print run. In some situations, it is necessary to print the desired color using a single ink or a spot color.
Various materials and techniques can be used in the printing process which can be considered for data hiding for spot colors and process colors, these materials include: substrate, process colors, overprinting, spot colors, spot tint (screening) and process equivalent tints. In printing, the term “substrate” refers to a base material which a design is printed onto. Most often, a substrate comprises paper which can be a variety of weights and finishes. Other common substrates in commercial printing include films, plastics, laminated plastics and foils.
Some additional color science background along with our improvements and additions are provided, below.
The color of an object is often the result of an interaction between a light source, an object and a detector (e.g., the human visual system). Other detectors include point of sale captured systems, mobile phone cameras, barcode readers, etc.
Light is radiation which can be seen, in the wavelength range of about 380 to 780 nm.
Spectral reflectance can be used to describe how an object interacts with light. When reflected light is detected and interpreted through the human visual system it results in an object having a particular color. The most common way to capture spectral data with a device is by using a spectrophotometer.
FIG. 1A shows spectral reflectance of PANTONE process color inks as measured using an i1Pro spectrophotometer, from X-Rite Corporation, headquartered in Grand Rapids, Mich., USA. FIG. 1A also shows spectrum emitted by red LED illumination at or around 660 nm. FIG. 1B shows 931 CIE 2° standard observer matching functions used for converting spectral reflectance to CIE XYZ color space.
Often color is described by artists and designers in terms of mixing paint or inks. An artist often starts with white paper, which reflects most of the light. Different colored pigments are applied on top of the paper, which reduce the amount of light reflected back. Current trends for printing describe subtractive four color mixing using process color combinations of CMYK. Yellow, for instance, reflects most of the light, it absorbs only the lower wavelengths.
In 1931, the CIE (Commission Internationale de l'Eclairage) developed a way to link between wavelengths in the visible spectrum and colors which are perceived by the human visual system. The models which the CIE developed made it possible to transform color information between physical responses to reflectance in color inks, illuminated displays, and capture devices such as digital cameras into a perceptually (nearly) uniform color space. The CIE XYZ color space was derived by multiplying the color matching functionst with the spectral power of the illuminant and the reflectance of an object, which results in a set of XYZ tristimulus values for a given sample. Within the CIE model, CIE Y describes the luminance or perceived brightness. While the CIE X and CIE Z plane contain the chromaticities, which describes the color regardless of luminance.
Chromaticity can be described by two parameters, hue and colorfulness. Hue or hue angle, describes the perceived color name, such as: red, green, yellow and blue. Colorfulness is the attribute which describes a color as having more or less of its hue. A color with 0 colorfulness would be neutral. The CIE took the CIE XYZ space to propose a pseudo-uniform color space, where calculated differences are proportional to perceptual differences between two color stimuli, formally referred to as the CIE 1976 L* a* b* (CIELAB) color space. The L* coordinate represents the perceived lightness, an L* value of 0 indicates black and a value of 100 indicates white. The CIE a* coordinate position goes between “redness” (positive) and “greenness” (negative), while the CIE b* goes between “yellowness” (positive) and “blueness” (negative).
To describe how perceptually similar two colors are, the CIE developed a color difference model or color error, CIE ΔE76. The first model developed was simply the Euclidean distance in CIELAB between two color samples. Since then, other more complex models have been developed to address some of the non-uniformity within the CIELAB Color-space, most notably the sensitivity to neutral or near neutral colors.
The CIELAB color difference metric is appropriate for measuring the color difference of a large uniform color region, however, the model does not consider the spatial-color sensitivity of the human eye. The luminance and chrominance CSF (Contrast Sensitivity Function) of the human visual system has been measured for various retinal illumination levels. The luminance CSF variation was measured by van Nes and Bouman (1967) and the chrominance CSF variation by van der Horst and Bouman (1969) and the curves are plotted in FIG. 1A and FIG. 1B for a single typical illumination level.
Ink overprint models predict final color obtained by overprinting several inks on a specific press and substrate. These models can be used by a digital watermark embedding algorithm to predict (1) color of the overprint for visibility evaluation, and (2) color of the overprint as seen by the imaging device for signal robustness evaluation.
Ink overprint models can be obtained in practice by combining two factors (1) set of measured color patches printed on a real press, and (2) mathematical model interpolating the measured values while making some simplifying assumptions. One model can be obtained by measuring a set of color patches obtained by sampling the space of all possible ink combinations, possibly printed multiple times and averaged. For example, for k inks and n steps of each ink, nk color patches would have to be printed and measured.
This process, known as press profiling or press fingerprinting, can be used with process inks, where a few thousand patches are used to characterize the press. Measured values are then interpolated and assembled into k-dimensional look-up table which is further consumed by software tools. ICC profiles are standardized and industry-accepted form of such look-up tables converting k ink percentages into either CIE XYZ or CIELAB space. For process inks, 4-channel CMYK profiles are standardized to maintain consistency between different printers. For example, the GRACoL (“General Requirements for Applications in Commercial Offset Lithography”) specification includes CMYK ICC profiles recommended for commercial offset lithography.
One aspect of the disclosure is a method comprising: receiving a digital design comprising an encoded signal carrying a plural-bit identifier, the design including an area of layered colors; a blend module for generating reflectance spectra estimates for the digital design including the area of layered colors, the reflectance spectra estimates provided on a per pixel basis for the design; generating a grayscale image representation of the encoded digital design from the reflectance spectra estimates at or around 660 nm; transforming the grayscale image representation according to an object form factor, said transforming yielding a transformed grayscale image; generating a representation of encoded signal detectability from the transformed grayscale image; producing a visibility map of a color image representation of the encoded digital design generated from a weighted sum of reflectance spectra estimates on a per pixel basis; and providing the representation of encoded signal detectability and the visibility map to influence signal encoding.
Another aspect of the disclosure is a system comprising: an input to receive an encoded design file, the design file comprising an encoded signal carrying a plural-bit identifier, the design file comprising a design including an area of layered colors; a blend module for operating on the encoded design file, the blend module generating reflectance spectra estimates for the design, the reflectance spectra estimates provided on a per pixel basis for the design; an image warping module for transforming a color image representation generated from a weighted sum of the reflectance spectra estimates, the image warping module transforming the design according to an object form factor; a signal strength module for producing a representation of encoded signal detectability, the signal strength module operating on a grayscale image representation generated from the reflectance spectra estimates at or around 660 nm; and a visibility module for generating a visibility map of color image representation generated from a weighted sum of the reflectance spectra estimates.
The above modules can be implemented as software modules for executing on one or more multi-core processors. In another implementation, the modules are provided as dedicated circuitry, e.g., as discussed in section III. Operating Environments, below.
Further combinations, aspects, features and description will become even more apparent with reference to the following detailed description and accompanying drawings. (Color drawings corresponding to the below FIG. 1A-FIG. 3 can be found in the Patent file of our U.S. Pat. No. 9,380,186, which is hereby incorporated herein by reference in its entirety.)